train3.py

import json
import pdb
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, OlmoeForCausalLM, OlmoeModel
from transformers.models.auto.modeling_auto import MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES
import copy
from transformers.modeling_outputs import (
    MoeCausalLMOutputWithPast,
    MoeModelOutputWithPast,
)
from collections import defaultdict
import numpy as np
import math
from torch import nn
import pandas as pd
from transformers.cache_utils import Cache, DynamicCache, StaticCache
from dataclasses import dataclass
# from transformers.models.olmoe.configuration_olmoe import OlmoeConfig
# from transformers.models.olmoe.modeling_olmoe import OlmoeMLP, OlmoeAttention, OlmoeFlashAttention2, OlmoeSdpaAttention, OlmoeRMSNorm, OlmoeSparseMoeBlock, apply_rotary_pos_emb, repeat_kv, OlmoeRotaryEmbedding
import os
import sys
import torch.distributed as dist
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
import transformers
import pickle

# from transformers.models.olmoe.configuration_olmoe import OlmoeConfig
from dataset import *
# from utils import flash_attn_forward, flash_attn_prepare_decoder_attention_mask, get_multiround_data
from peft import (get_peft_model, PeftModel)
import random
# from config import *
from datasets import Dataset, DatasetDict, load_dataset
import wandb
import gc
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import functools
from torch.optim.lr_scheduler import StepLR
import torch.nn.functional as F
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler

from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
    checkpoint_wrapper, CheckpointImpl)

from torch.distributed.fsdp import (
    FullyShardedDataParallel as FSDP,
    MixedPrecision,
    BackwardPrefetch,
    ShardingStrategy,
    FullStateDictConfig,
    StateDictType,
)
from torch.distributed.fsdp.wrap import (
    transformer_auto_wrap_policy,
    enable_wrap,
    wrap,
)
from functools import partial
from torch.utils.data import DataLoader
from pathlib import Path
from typing import Type, List, Optional, Tuple, Union
from modelforseminat_v4 import *

from torch.optim.lr_scheduler import _LRScheduler

class WarmupCosineScheduler(_LRScheduler):

    def __init__(self,
                 optimizer,
                 warmup_steps,
                 total_steps,
                 min_lr=0.0,
                 last_epoch=-1):
        # self.warmup_steps = warmup_steps
        self.total_steps = total_steps
        self.min_lr = min_lr
        if isinstance(warmup_steps, float) and 0 < warmup_steps < 1:
            self.warmup_steps = int(warmup_steps * total_steps)
        else:
            self.warmup_steps = int(warmup_steps)
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        step = self.last_epoch + 1
        lrs = []

        for base_lr in self.base_lrs:
            if step < self.warmup_steps:
                # Linear warmup
                lr = base_lr * step / self.warmup_steps
            else:
                # Cosine decay
                progress = (step - self.warmup_steps) / max(
                    1, self.total_steps - self.warmup_steps)
                cosine_decay = 0.5 * (1 + math.cos(math.pi * progress))
                lr = self.min_lr + (base_lr - self.min_lr) * cosine_decay

            lrs.append(lr)

        return lrs



################################# FSDP Config #####################################
def setup():
    # initialize the process group
    local_rank = int(os.environ['LOCAL_RANK'])
    torch.cuda.set_device(local_rank)
    dist.init_process_group(
          backend='nccl',
          init_method='env://',
           )


def cleanup():
    gc.collect()
    torch.cuda.empty_cache()
    dist.destroy_process_group()


def get_fsdp_device():
    # 每个进程初始化分布式环境后调用
    local_rank = int(os.environ.get("LOCAL_RANK", 0))  # torchrun 自动设置
    device = torch.device(f"cuda:{local_rank}")
    torch.cuda.set_device(device)
    return device


def load_trained_model(model_name):
    DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

    olmo_path = "/AIRvePFS/ai4science/users/ai4science/users/zyk/seminat_backup/model/OLMo-2-0425-1B"
    pt_path = "/AIRvePFS/ai4science/users/ai4science/users/zyk/seminat/ckp/sft-v4-1e3-len2-fc-chunklimit2-jueduipos/sft-v4-1e3-len2-fc-chunklimit2-jueduipos-steps_300.pt"
    config_path = "/AIRvePFS/ai4science/users/ai4science/users/zyk/seminat_backup/model/OLMo-2-0425-1B/config.json"


    config = AutoConfig.from_pretrained(olmo_path)
    model = Olmo2ForCausalLMForSemiNAT.from_pretrained(olmo_path,
                                                    config=config,
                                                    torch_dtype=torch.bfloat16)
    state_dict = torch.load(pt_path, map_location=DEVICE, weights_only=True)
    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
    print(
        f"Loaded with {len(missing_keys)} missing keys and {len(unexpected_keys)} unexpected keys."
    )
    if missing_keys:
        print("Missing keys:", missing_keys)
    if unexpected_keys:
        print("Unexpected keys:", unexpected_keys)

    model = model.to(DEVICE)

    tokenizer = AutoTokenizer.from_pretrained(olmo_path)

    return model, tokenizer


def setup_model(model_name):
    model = Olmo2ForCausalLMForSemiNAT.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    # config = AutoConfig.from_pretrained(model_name)
    # model = Olmo2ForCausalLMForSemiNAT(config)  # 注意这里不用 from_pretrained
    # tokenizer = AutoTokenizer.from_pretrained(model_name)

    return model, tokenizer


def fsdp_main(args):
    local_rank = int(os.environ['LOCAL_RANK'])
    rank = int(os.environ['RANK'])
    world_size = int(os.environ['WORLD_SIZE'])
    if args.use_wandb and rank == 0:
        wandb.init(entity="SemiNAT", project="SemiNAT-SFT", name=args.run_name)

    model, tokenizer = setup_model(args.model_path)
    # model, tokenizer = load_trained_model(args.model_path)

    model.config.chunk_size_limit = args.chunk_size_limit

    # pdb.set_trace()

    if ".pkl" in args.data_path:
        train_dataset = pickle.load(open(args.data_path, "rb"))
    else:
        datasets = pd.read_parquet(args.data_path)
        train_dataset = eval(f"{args.data_type}")(
            tokenizer,
            datasets,  # your data preprocessing function
            args.max_length,  # your max input length
            args.data_processess_num)

    train_sampler = DistributedSampler(train_dataset,
                                       rank=rank,
                                       num_replicas=world_size,
                                       shuffle=True)
    train_dataloader = DataLoader(dataset=train_dataset,
                                  sampler=train_sampler,
                                  batch_size=args.batch_size)

    print(f"Size of train dataset: {len(train_dataset)}")

    setup()

    Olmo2DecoderLayerForSemiNAT_auto_wrap_policy = functools.partial(
        transformer_auto_wrap_policy,
        transformer_layer_cls={
            Olmo2DecoderLayerForSemiNAT,
            NATDecoderForSemiNAT,
        })

    sharding_strategy: ShardingStrategy = ShardingStrategy.FULL_SHARD  #for Zero2 and FULL_SHARD for Zero3
    torch.cuda.set_device(local_rank)

    bfSixteen = MixedPrecision(
        param_dtype=torch.bfloat16,
        reduce_dtype=torch.bfloat16,
        buffer_dtype=torch.bfloat16,
    )

    # if bf16_ready:
    mp_policy = bfSixteen
    # else:
    # mp_policy = None  # defaults to fp32

    # if args.use_lora:
    #     model = get_peft_model(model, lora_config)

    # model is on CPU before input to FSDP
    model = FSDP(model,
                 auto_wrap_policy=Olmo2DecoderLayerForSemiNAT_auto_wrap_policy,
                 mixed_precision=mp_policy,
                 sharding_strategy=sharding_strategy,
                 device_id=torch.cuda.current_device(),
                 use_orig_params=True)

    optimizer = optim.AdamW(
        model.parameters(),
        lr=args.lr,
        betas=args.betas,
        weight_decay=args.weight_decay,
        eps=args.eps,
    )

    # scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
    scheduler = WarmupCosineScheduler(
        optimizer=optimizer,  # 优化器对象
        warmup_steps=args.warmup_steps,  # warmup 步数（或比例）
        total_steps=args.total_steps,  # 总训练步数
        min_lr=args.min_lr  # 最小学习率
    )

    torch.autograd.set_detect_anomaly(True)

    loss1_list = []
    loss2_list = []
    loss_list = []

    global_step = 0
    for epoch in range(1, args.epochs + 1):
        # t0 = time.time()
        model.train()
        local_rank = int(os.environ['LOCAL_RANK'])  
        # fsdp_loss = torch.zeros(2).to(local_rank)

        if train_sampler:
            train_sampler.set_epoch(epoch)
        if rank == 0:
            inner_pbar = tqdm(range(len(train_dataloader)),
                              colour="blue",
                              desc="r0 Training Epoch")
        for batch in train_dataloader:
            optimizer.zero_grad()
            loss1, loss2 = model(input_ids=batch[0],
                                 labels=batch[1],
                                 attention_mask=batch[2],
                                 slice_pos=batch[3],
                                 use_cache=False).loss
            loss = loss1 + loss2
            # loss = loss2
            loss1_list.append(loss1.item())
            loss2_list.append(loss2.item())
            loss_list.append(loss.item())
            # pdb.set_trace()

            # if torch.isnan(loss):
            #     print(f"Step {global_step}: loss is NaN, entering pdb …")
            #     pdb.set_trace()

            # print(f"loss1:{loss1},loss2:{loss2}")
            loss.backward()

            # 按参数计算
            for name, module in model.named_modules():
                total_norm = 0.0
                param_count = 0
                for param in module.parameters(recurse=False):
                    if param.grad is not None:
                        total_norm += param.grad.data.norm(2).item()**2
                        param_count += 1
                if param_count > 0:
                    total_norm = total_norm**0.5
                    wandb.log({f"grad_norm/{name}": total_norm},
                              step=global_step)

            # 按层计算
            # layer_grads = defaultdict(list)
            # for name, module in model.named_modules():
            #     # 只处理 encoder.layers.N 这类结构（按需改写匹配条件）
            #     # pdb.set_trace()
            #     if "model.layers." in name or "model.decoder" in name or "model.encoder" in name:
            #         if "model.layers" in name:
            #             # 提取层号（如 encoder.layers.0 → layer0）
            #             parts = name.split(".")
            #             try:
            #                 layer_idx = int(parts[3])
            #             except (IndexError, ValueError):
            #                 pdb.set_trace()
            #                 # continue

            #             layer_key = f"layer{layer_idx}"
            #         else:
            #             layer_key = "decoder" if "model.decoder" in name else "encoder"

            #         # 收集该模块下的参数梯度（只自身，不递归）
            #         for param in module.parameters(recurse=False):
            #             if param.grad is not None:
            #                 layer_grads[layer_key].append(
            #                     param.grad.detach().flatten())
            #     if "lm_head" in name:
            #         layer_key = "lm_head"
            #         layer_grads[layer_key].append(
            #             param.grad.detach().flatten())

            # # 合并每层所有子模块的梯度，并 log 总体 L2 范数
            # for layer_key, grads in layer_grads.items():
            #     if grads:
            #         total_grad = torch.cat(grads).norm(2).item()
            #         wandb.log({f"grad_norm/{layer_key}": total_grad},
            #                   step=global_step)

            optimizer.step()

            global_step += 1

            if global_step % args.save_steps == 0:
                save_policy = FullStateDictConfig(offload_to_cpu=True,
                                                  rank0_only=True)
                with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT,
                                          save_policy):
                    cpu_state = model.state_dict()

                if rank == 0:
                    print(f"--> steps: {str(global_step)} saving model ...")
                    if not os.path.exists(args.save_path):
                        os.makedirs(args.save_path)
                    save_name = f"{args.save_name}-steps_{str(global_step)}.pt"
                    print(f"--> saving as model name {save_name}")
                    save_path = os.path.join(args.save_path, save_name)
                    torch.save(cpu_state, save_path)

            if rank == 0:
                inner_pbar.update(1)
                if args.use_wandb:
                    wandb.log({
                        "length prediction loss":
                        sum(loss1_list[-20:]) / len(loss1_list[-20:]),
                        "nat loss":
                        sum(loss2_list[-20:]) / len(loss2_list[-20:]),
                        "loss":
                        sum(loss_list[-20:]) / len(loss_list[-20:])
                    })

        dist.all_reduce(loss, op=dist.ReduceOp.SUM)

        if rank == 0:
            inner_pbar.close()

        scheduler.step()

        # if rank == 0:
        #     print(f"--> entering save model state")

        # save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
        # with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT,
        #                           save_policy):
        #     cpu_state = model.state_dict()

        # if rank == 0:
        #     print(f"--> epoch: {str(epoch)} saving model ...")
        #     if not os.path.exists(args.save_path):
        #         os.makedirs(args.save_path)
        #     save_name = f"{args.save_name}-epoch_{str(epoch)}.pt"
        #     print(f"--> saving as model name {save_name}")
        #     save_path = os.path.join(args.save_path, save_name)
        #     torch.save(cpu_state, save_path)

    dist.barrier()
    cleanup()


################################# FSDP Config #####################################

if __name__ == "__main__":
    # Training settings
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch-size',
                        type=int,
                        default=4,
                        metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--model_path', type=str)
    parser.add_argument('--save_path', type=str)
    parser.add_argument('--save_name', type=str)
    parser.add_argument('--data_path', type=str)
    parser.add_argument('--data_type', type=str)
    parser.add_argument('--run_name', type=str)
    parser.add_argument('--max_length', type=int)
    parser.add_argument('--chunk_size_limit', type=int)
    parser.add_argument('--save_steps', type=int, default=5000)
    parser.add_argument('--data_processess_num', type=int, default=8)
    parser.add_argument('--epochs',
                        type=int,
                        default=2,
                        metavar='N',
                        help='number of epochs to train (default: 3)')
    parser.add_argument('--lr',
                        type=float,
                        default=.002,
                        metavar='LR',
                        help='learning rate (default: .002)')
    parser.add_argument('--gamma',
                        type=float,
                        default=0.7,
                        metavar='M',
                        help='Learning rate step gamma (default: 0.7)')
    parser.add_argument('--weight_decay', type=float)
    parser.add_argument('--eps', type=float)
    parser.add_argument('--decay_norm_and_bias', type=bool)
    parser.add_argument(
        "--betas",
        type=float,
        nargs=2,  # 表示需要两个 float 值
        default=[0.9, 0.999]  # 可选默认值
    )
    parser.add_argument('--decay_embeddings', type=bool)
    parser.add_argument('--warmup_steps', type=float)
    parser.add_argument('--total_steps', type=int)
    parser.add_argument('--min_lr', type=float)
    parser.add_argument('--seed',
                        type=int,
                        default=1,
                        metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--use_lora', action='store_true', default=False)
    parser.add_argument("--use_wandb",
                        action="store_true",
                        help="whether to use wandb")

    args = parser.parse_args()

    torch.manual_seed(args.seed)

    fsdp_main(args)